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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 851–875 of 2111 papers

TitleStatusHype
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse—0
Do "New Snow Tablets" Contain Snow? Large Language Models Over-Rely on Names to Identify Ingredients of Chinese DrugsCode0
Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation—0
Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation—0
OnRL-RAG: Real-Time Personalized Mental Health Dialogue System—0
CoRAG: Collaborative Retrieval-Augmented Generation—0
GeoRAG: A Question-Answering Approach from a Geographical Perspective—0
From Code Generation to Software Testing: AI Copilot with Context-Based RAG—0
One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image—0
PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood EstimationCode0
GTR: Graph-Table-RAG for Cross-Table Question Answering—0
Reasoning LLMs for User-Aware Multimodal Conversational Agents—0
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph—0
Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding—0
AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening—0
CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation—0
LLM-Assisted Proactive Threat Intelligence for Automated Reasoning—0
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems—0
Command A: An Enterprise-Ready Large Language Model—0
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework—0
Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation—0
ScholarCopilot: Training Large Language Models for Academic Writing with Accurate Citations—0
Medical large language models are easily distractedCode0
CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
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